Land Cover Classification in the Deschutes River – Capitol Lake Subwatershed, WA¶

Introduction¶

Land-cover classification is an important tool for natural resource management, as it impacts many other ecological functions and processes. Types of land cover have different impacts on water quality, pollution, resource availability, climate, habitat, and more (U.S. EPA, 2025). Understanding the types of land cover and how they are changing over time is important to making land use, conservation, and planning decisions. Thurston County is a growing county in the U.S. and is projected to experience major growth over the next twenty years. According to the WA Department of Ecology (n.d.), the Deschutes watershed has known water quality issues associated with development, land use, and ongoing pollution. The Deschutes watershed in Thurston County is a useful case study for land-cover classification, as it is a mixed-use watershed. Understanding land cover classification from spatial data helps to inform decisions for sustainable growth and changing land use. We can explore land cover through satellite imagery and remote sensing. This analysis applies an unsupervised machine learning algorithm (k-means clustering) to identify clusters based on spectral reflectance values and explore potential patterns in land cover types such as water, forest, urban, and agriculture.

Study Area¶

The Deschutes River originates in the Bald Hills in Lewis County, where it flows 57 miles through Thurston County to Tumwater Falls and into Capitol Lake, a managed reservoir & estuarine system in the South Puget Sound region. The Deschutes is one of the largest river systems in Thurston County and is a watershed that contains a variety of land uses, including agriculture, recreational, urban, and forest lands.

The subwatershed used for this analysis was HUC12 171100160202 (Deschutes River - Capitol Lake) from the U.S. Geological Survey National Watershed Boundary Dataset. With ongoing development in the area and projected growth of 30% by 2045 (Thurston 2045 Comprehensive Plan), this watershed is a good case study for examining land-cover classifications in the region.

Methods¶

For this analysis, cloud masking was performed to remove atmospheric interference and irregularities to ensure that accurate surface reflectance values were included. Multispectral imagery (harmonized Sentinel/Landset data) from the NASA Earth Data platform was used to get the surface reflectance values from spectral bands / wavelengths. The satellite imagery used was from June to August 2024. These raster images were then stacked to give a complete picture of surface reflectance across spectral wavelengths. These differences in surface reflectance can be used to look at land-cover classifications.

The spectral reflectance values were then used as an input feature for an unsupervised machine learning clustering algorithm, k-means. As an unsupervised model, it does not use training data input. We applied the k-means clustering algorithm from the scikit-learn library, which groups pixels based on this spectral similarity. K-means clusters the data based on a selected number of clusters. In this analysis, six clusters were used, as this was an exploratory analysis for land classification. K-means cluster values of five through eight were tested, and six was the most clear with the least amount of visual noise. Six clusters are a typical amount expected for land-cover classification. K-means classifies (without labels) the number of clusters into similar groups based on spectral similarity.

K-means is a useful tool for land-cover classification because it doesn’t require training data. Different landscape features (water, impervious surface, forest, etc.) have different reflectance signatures that can be classified without bias and directly from the data in the satellite imagery and raster dataset. We can then make determinations with background knowledge and maps from the area to make land-cover interpretations. The land-cover inferences are speculative and exploratory in nature, due to the unsupervised nature of K-means.

Data Citations¶

  • Masek, J., Ju, J., Roger, J.-C., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., & Justice, C. (2021). HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 [Data set]. NASA Land Processes Distributed Active Archive Center. https://doi.org/10.5067/HLS/HLSL30.002 Date Accessed: 2026-02-16

  • U.S. Geological Survey, 2024, National watershed boundary (HUC12) dataset for the conterminous United States, retrieved [02/16/2026], from https://data.usgs.gov/datacatalog/data/USGS:63d31e73d34e06fef1501265.

STEP 1: Set up¶

Step 1a: Load libraries and set GDAL parameters¶

In [1]:
### Import libraries
# Create reproducible file paths
import os

# Serializing and unserializing objects (save objects to disk and load later)
import pickle

# Regular expressions
import re

# Give warnings
import warnings

# Project coordinate systems for spatial data and mapping
import cartopy.crs as ccrs

# Access satellite imagery through the NASA API
import earthaccess

# Spatial data analysis
import earthpy as et

# Work with vector/shapefiles
import geopandas as gpd

# Vizualization tools
import geoviews as gv
import hvplot.pandas
import hvplot.xarray

# Work with arrays
import numpy as np

# Work with tabular data
import pandas as pd

# Work with raster data
import rioxarray as rxr
import rioxarray.merge as rxrmerge
import xarray as xr

# Use progress bar
from tqdm.notebook import tqdm
from ipywidgets import IntProgress
from IPython.display import display

# Polygons
from shapely.geometry import Polygon

# Use kmeans clustering
from sklearn.cluster import KMeans

### set GDAL (Geospatial Data Abstraction Library) parameters
os.environ["GDAL_HTTP_MAX_RETRY"] = "5"
os.environ["GDAL_HTTP_RETRY_DELAY"] = "1"

### don't show non-critical warnings
warnings.simplefilter('ignore')

c:\Users\nymve\miniconda3\envs\earth-analytics-python\Lib\site-packages\earthpy\__init__.py:7: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
  from pkg_resources import resource_string

Step 1b: Run the caching decorator¶

In [2]:
### make the caching decorator
def cached(func_key, override=False): # Add =True if wanting to rerun
    """
    A decorator to cache function results
    
    Parameters
    ==========
    key: str
      File basename used to save pickled results
    override: bool
      When True, re-compute even if the results are already stored
    """
    def compute_and_cache_decorator(compute_function):
        """
        Wrap the caching function
        
        Parameters
        ==========
        compute_function: function
          The function to run and cache results
        """
        def compute_and_cache(*args, **kwargs):
            """
            Perform a computation and cache, or load cached result.
            
            Parameters
            ==========
            args
              Positional arguments for the compute function
            kwargs
              Keyword arguments for the compute function
            """
            ### Add an identifier from the particular function call
            if 'cache_key' in kwargs:
                key = '_'.join((func_key, kwargs['cache_key']))
            else:
                key = func_key

            ### define a file path based on the directory structure in earthpy
            path = os.path.join(
                
                ### earthpy directory
                et.io.HOME, 
                
                ### earthpy dataset
                et.io.DATA_NAME, 
                
                ### make a subdirectory called "jars"
                'jars', 
                
                ### use f-string (formatted string) to create a string by embedding the value
                ### of the variable "key" into the string 
                ### use .pickle file extension (a pickle file is a serialized python objecT)
                f'{key}.pickle')
            
            ### Check if the cache exists already or if we should override caching
            if not os.path.exists(path) or override:
                
                ### Make jars directory if needed
                os.makedirs(os.path.dirname(path), exist_ok=True)
                
                ### Run the compute function as the user did
                result = compute_function(*args, **kwargs)
                
                ### Pickle the object (save to file)
                ### open the file at filename (wb - write binary)
                with open(path, 'wb') as file:
                    
                    ### save the result without needing to recompute when loading
                    ### it back into Python
                    pickle.dump(result, file)
            
            ### if the file already exists/we are not overriding the cache
            else:
               
                ### Unpickle the object (load the cached result) (rb - read binary)
                with open(path, 'rb') as file:
                    
                    ### use pickle.load to unserialize the file back into a python object
                    result = pickle.load(file)
                    
            return result
        
        return compute_and_cache
    
    return compute_and_cache_decorator

STEP 2: Study site¶

In [3]:
# Assign the hydrologic unit code
HUC_LEVEL = 12

# Download, unzip, and read shapefile, using cache decorator
@cached(f'wbd_17_hu{HUC_LEVEL}_gdf')

# Make a function to read in the file
def read_wbd_file(wbd_filename, cache_key=None):

    # Define the URL we're pulling data from
    wbd_url = (

        ### add base URL
        "https://prd-tnm.s3.amazonaws.com/StagedProducts/Hydrography/WBD/HU12/Shape/"

        # Insert the name of the specific file we want
        f"{wbd_filename}.zip"
    )

    # Download data and unzip it into the directory
    wbd_dir = et.data.get_data(url = wbd_url)

    # Path to shapefile in the fir
    wbd_path = os.path.join(wbd_dir,
                            'Shape',                            
                            f'WBDHU{HUC_LEVEL}.shp')
    
    # Read the shp as gdf
    wbd_gdf = gpd.read_file(wbd_path,
                            
                            # Use pyogrio library
                            engine = 'pyogrio')
    
    # Give us the gdf for the watershed boundary
    return wbd_gdf
In [4]:
# open the shapefile using the read_wbd_file function that we created
wbd_gdf = read_wbd_file("WBD_17_HU2_Shape",
                        f'hu{HUC_LEVEL}')
In [5]:
# Look at data
wbd_gdf
Out[5]:
tnmid metasource sourcedata sourceorig sourcefeat loaddate referenceg areaacres areasqkm states ... name hutype humod tohuc noncontrib noncontr_1 shape_Leng shape_Area ObjectID geometry
0 {0E4E6573-8B28-4F8C-B959-694DE3A9F91C} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1148597 12257.36 49.60 OR ... Rock Creek S NM 170900050403 0.0 0.000000 NaN NaN 1 POLYGON ((-122.44236 44.75208, -122.44279 44.7...
1 {07FD06B3-4536-4B3D-A598-8D646D308101} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1127639,1147060 21300.40 86.20 OR ... Stout Creek-North Santiam River M NM 170900050602 0.0 NaN NaN NaN 2 POLYGON ((-122.54082 44.83544, -122.54007 44.8...
2 {8720D0B0-BEF6-492F-B878-1B0343DB67DD} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1519865 17826.81 72.14 CN ... Lower Galbraith Creek S NM 170101010306 NaN NaN NaN NaN 3 POLYGON ((-115.22856 49.74735, -115.22856 49.7...
3 {F673354E-0002-4C26-8E80-CFA9C84F31AA} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1146937 15583.39 63.06 OR ... Headwaters North Fork Crooked River S DD,IT,CD 170703040308 0.0 0.000000 NaN NaN 4 POLYGON ((-120.19341 44.34757, -120.1938 44.34...
4 {546E09D7-5785-4B5B-A284-634E682D7C41} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 384804,388149 19147.98 77.49 ID ... Little Deer Creek-Panther Creek S NM 170602031201 0.0 0.000000 NaN NaN 5 POLYGON ((-114.22983 45.20654, -114.2297 45.20...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8928 {2CEBB4FF-1A8E-4DA4-94EF-2B790B6C3117} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1162590 12778.53 51.71 OR ... Corral Spring C NM CLOSED BASIN 12771.0 51.682449 NaN NaN 8929 POLYGON ((-121.70858 43.26795, -121.70861 43.2...
8929 {367A4656-FF42-48AB-A264-5C8B038FE546} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 374733 23055.80 93.30 ID ... Rock Lakes S ID,GC,CD 170402150505 0.0 0.000000 NaN NaN 8930 POLYGON ((-112.35436 44.08689, -112.35466 44.0...
8930 {076A17A9-ACFD-4833-A00B-00B72882C5BC} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 377793 13336.23 53.97 ID ... Blackbird Creek S NM 170602031105 0.0 0.000000 NaN NaN 8931 POLYGON ((-114.2675 45.09587, -114.2672 45.095...
8931 {67211EB6-B26C-4553-B6DF-B576BA652C5C} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1149920 18962.85 76.74 OR ... Upper South Fork Alsea River S NM 171002050104 0.0 0.000000 NaN NaN 8932 POLYGON ((-123.48253 44.40587, -123.48237 44.4...
8932 {B609D074-6302-43A5-A9EA-49B2E7A10681} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1141306,1151904 23695.41 95.89 OR ... Dry Creek-Warm Springs River S NM 170703060602 0.0 0.000000 NaN NaN 8933 POLYGON ((-121.71836 45.03733, -121.71765 45.0...

8933 rows × 21 columns

In [6]:
### Filter the shapefile to the specific watershed we're using

# Define the gdf for the watershed by subsetting the gdf of the whole watershed dataset
deschutes_gdf = wbd_gdf[wbd_gdf[
                
    # Filter the gdf to the row(s) with the watershe we want 
    # Use "dissolve" to merge the geometries of all the rows matching the target watershed            
    f'huc{HUC_LEVEL}'].isin(['171100160202'])].dissolve() 



### check it out
deschutes_gdf
Out[6]:
geometry tnmid metasource sourcedata sourceorig sourcefeat loaddate referenceg areaacres areasqkm ... huc12 name hutype humod tohuc noncontrib noncontr_1 shape_Leng shape_Area ObjectID
0 POLYGON ((-122.93753 47.06808, -122.93588 47.0... {694A30A3-78CE-48CA-9267-A52677CB0673} {511D2AC8-11BA-45FC-AB98-F69D693D4C44} Watershed Boundary Dataset (WBD) Natural Resources and Conservation Service and... None 2024-08-15 1518684,1513902 24427.95 98.86 ... 171100160202 Deschutes River-Capitol Lake S NM 171100190900 0.0 0.0 NaN NaN 163

1 rows × 21 columns

In [7]:
### Make a site map with satellite imagery in the background
(
    # Project the delta_gdf to Mercator
    deschutes_gdf.to_crs(ccrs.Mercator())

    # Use hvplot
    .hvplot(

        # Make the watershed transparent
        alpha = 0.40, fill_color = "white",

        # Add satellite basemap
        tiles = 'EsriImagery',

        crs = ccrs.Mercator())

        # Set plot size
        .opts(width = 600, height = 300)
)
Out[7]:

STEP 3: Multispectral data¶

Step 3a: Search for data¶

In [8]:
### Log in to earthaccess
earthaccess.login(persist = True)
Out[8]:
<earthaccess.auth.Auth at 0x2419f7b2690>
In [9]:
### Search for HLS granules we want
results = earthaccess.search_data(

    ### Specify which dataset and spatial resolution we want 
    short_name = "HLSL30",

    ### Specify that we're using cloud data
    cloud_hosted = True,

    ### Use the bounding box from our watershed boundary
    bounding_box = tuple(deschutes_gdf.total_bounds),

    ### set the temporal range of the data
    temporal = ("2024-06", "2024-08")
)
In [10]:
results
Out[10]:
[Collection: {'EntryTitle': 'HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0'}
 Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -122.33034656, 'Latitude': 46.86374115}, {'Longitude': -121.92318169, 'Latitude': 47.84865186}, {'Longitude': -123.00026736, 'Latitude': 47.85370184}, {'Longitude': -123.00026241, 'Latitude': 46.86569987}, {'Longitude': -122.33034656, 'Latitude': 46.86374115}]}}]}}}
 Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2024-06-06T19:00:55.253Z', 'EndingDateTime': '2024-06-06T19:00:55.253Z'}}
 Size(MB): 110.67294883728027
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 Collection: {'EntryTitle': 'HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0'}
 Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -122.69050447, 'Latitude': 45.96513207}, {'Longitude': -122.29455827, 'Latitude': 46.95153596}, {'Longitude': -123.00026284, 'Latitude': 46.95370918}, {'Longitude': -123.00025813, 'Latitude': 45.96555115}, {'Longitude': -122.69050447, 'Latitude': 45.96513207}]}}]}}}
 Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2024-06-06T19:00:55.253Z', 'EndingDateTime': '2024-06-06T19:01:19.140Z'}}
 Size(MB): 65.16892051696777
 Data: ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B03.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.SZA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B06.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.SAA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.VAA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B04.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B10.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B05.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B02.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B09.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B01.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B07.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.Fmask.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.VZA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TES.2024158T190055.v2.0/HLS.L30.T10TES.2024158T190055.v2.0.B11.tif'],
 Collection: {'EntryTitle': 'HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0'}
 Spatial coverage: {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -124.3124115, 'Latitude': 46.85817596}, {'Longitude': -122.87194284, 'Latitude': 46.86562825}, {'Longitude': -122.86952964, 'Latitude': 47.85362771}, {'Longitude': -124.33713471, 'Latitude': 47.84591483}, {'Longitude': -124.3124115, 'Latitude': 46.85817596}]}}]}}}
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 Temporal coverage: {'RangeDateTime': {'BeginningDateTime': '2024-08-26T18:55:25.731Z', 'EndingDateTime': '2024-08-26T18:55:49.618Z'}}
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 Data: ['https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.VZA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.SAA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B09.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.VAA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B07.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B10.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.Fmask.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B03.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B02.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.SZA.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B06.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B01.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B11.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B05.tif', 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024239T185525.v2.0/HLS.L30.T10TET.2024239T185525.v2.0.B04.tif']]

Step 3b: Compile information about each granule¶

In [11]:
### Make a function to process all the granules from the earthaccess search
### And extract information for each granule

# Define the function
def get_earthaccess_links(results):

    # Make and display a progress bar
    f = IntProgress(min = 0, max = len(results), description = 'Open granules')
    display(f)

    # Use a regular expression to extract tile_id and bank from .tif files
    url_re = re.compile(
        r'\.(?P<tile_id>\w+)\.\d+T\d+\.v\d\.\d\.(?P<band>[A-Za-z0-9]+)\.tif')

    # Accumulate gdf rows from each granule
    link_rows = []

    # Loop over granules to extract info
    for granule in results:

        # Locate metadata (UMM = universal metadata model)
        info_dict = granule['umm']

        # Pull out unique identifier for the granule
        granule_id = info_dict['GranuleUR']

        # Extract date/time 
        datetime = pd.to_datetime(
            info_dict['TemporalExtent']['RangeDateTime']['BeginningDateTime']
        )

        # Extact boundary coordinates for granule
        points = (
            info_dict
            ['SpatialExtent']['HorizontalSpatialDomain']['Geometry']['GPolygons'][0]
            ['Boundary']['Points']
        )

        # Make polygon using coordinate points for granule
        geometry = Polygon(
            [(point['Longitude'],
              point['Latitude']) for point in points]
        )

        # Get url and open granule
        files = earthaccess.open([granule])

        # Loop through each file in the granule
        for file in files:

            # Use url regular expression to get url
            match = url_re.search(file.full_name)

            # If match is found, append data to link_rows gdf we initialized
            if match is not None: 
                link_rows.append(

                    # Makes a gdf with the granule's data and geometry
                    gpd.GeoDataFrame(
                        dict(

                        # Timestamp
                        datetime = [datetime],

                        # Unique ID
                        tile_id = [match.group('tile_id')],

                        # Spectral band name
                        band = [match.group('band')],

                        # url
                        url = [file],

                        geometry = [geometry]
                    ),

                    # Set crs
                    crs ="EPSG:4326"
                )
            )

        # Update progress bar after each granule is done
        f.value += 1

    # Combine into a single gdf   
    file_df = pd.concat(link_rows).reset_index(drop = True)

    # Return the final gdf file
    return file_df
In [12]:
granule = results[0]

granule
Out[12]:

Data: HLS.L30.T10TET.2024158T190055.v2.0.B06.tifHLS.L30.T10TET.2024158T190055.v2.0.B10.tifHLS.L30.T10TET.2024158T190055.v2.0.B01.tifHLS.L30.T10TET.2024158T190055.v2.0.VZA.tifHLS.L30.T10TET.2024158T190055.v2.0.VAA.tifHLS.L30.T10TET.2024158T190055.v2.0.B03.tifHLS.L30.T10TET.2024158T190055.v2.0.B07.tifHLS.L30.T10TET.2024158T190055.v2.0.B04.tifHLS.L30.T10TET.2024158T190055.v2.0.Fmask.tifHLS.L30.T10TET.2024158T190055.v2.0.B05.tifHLS.L30.T10TET.2024158T190055.v2.0.B09.tifHLS.L30.T10TET.2024158T190055.v2.0.SZA.tifHLS.L30.T10TET.2024158T190055.v2.0.B02.tifHLS.L30.T10TET.2024158T190055.v2.0.SAA.tifHLS.L30.T10TET.2024158T190055.v2.0.B11.tif

Size: 110.67 MB

Cloud Hosted: True

Data Preview
In [13]:
info_dict = granule['umm']
info_dict
Out[13]:
{'TemporalExtent': {'RangeDateTime': {'BeginningDateTime': '2024-06-06T19:00:55.253Z',
   'EndingDateTime': '2024-06-06T19:00:55.253Z'}},
 'GranuleUR': 'HLS.L30.T10TET.2024158T190055.v2.0',
 'AdditionalAttributes': [{'Name': 'LANDSAT_PRODUCT_ID',
   'Values': ['LC08_L1TP_047027_20240606_20240606_02_RT']},
  {'Name': 'CLOUD_COVERAGE', 'Values': ['6']},
  {'Name': 'MGRS_TILE_ID', 'Values': ['10TET']},
  {'Name': 'SPATIAL_COVERAGE', 'Values': ['60']},
  {'Name': 'SPATIAL_RESOLUTION', 'Values': ['30.0']},
  {'Name': 'SPATIAL_RESAMPLING_ALG', 'Values': ['Cubic Convolution']},
  {'Name': 'HLS_PROCESSING_TIME', 'Values': ['2024-06-25T18:12:32Z']},
  {'Name': 'SENSING_TIME', 'Values': ['2024-06-06T19:00:55.2536540Z']},
  {'Name': 'HORIZONTAL_CS_NAME', 'Values': ['UTM, WGS84, UTM ZONE 10']},
  {'Name': 'ULX', 'Values': ['499980.0']},
  {'Name': 'ULY', 'Values': ['5300040.0']},
  {'Name': 'ADD_OFFSET', 'Values': ['0']},
  {'Name': 'REF_SCALE_FACTOR', 'Values': ['0.0001']},
  {'Name': 'THERM_SCALE_FACTOR', 'Values': ['0.01']},
  {'Name': 'ANG_SCALE_FACTOR', 'Values': ['0.01']},
  {'Name': 'FILLVALUE', 'Values': ['-9999']},
  {'Name': 'QA_FILLVALUE', 'Values': ['255']},
  {'Name': 'NCOLS', 'Values': ['3660']},
  {'Name': 'NROWS', 'Values': ['3660']},
  {'Name': 'MEAN_SUN_AZIMUTH_ANGLE', 'Values': ['145.07391511']},
  {'Name': 'MEAN_SUN_ZENITH_ANGLE', 'Values': ['28.17015973']},
  {'Name': 'MEAN_VIEW_AZIMUTH_ANGLE', 'Values': ['281.71740559']},
  {'Name': 'MEAN_VIEW_ZENITH_ANGLE', 'Values': ['5.48908053']},
  {'Name': 'NBAR_SOLAR_ZENITH', 'Values': ['27.56970866']},
  {'Name': 'ACCODE', 'Values': ['LaSRC v3.5.1']},
  {'Name': 'TIRS_SSM_MODEL', 'Values': ['PRELIMINARY']},
  {'Name': 'TIRS_SSM_POSITION_STATUS', 'Values': ['ESTIMATED']},
  {'Name': 'USGS_SOFTWARE', 'Values': ['LPGS_16.4.0']},
  {'Name': 'IDENTIFIER_PRODUCT_DOI', 'Values': ['10.5067/HLS/HLSL30.002']},
  {'Name': 'IDENTIFIER_PRODUCT_DOI_AUTHORITY', 'Values': ['https://doi.org']}],
 'SpatialExtent': {'HorizontalSpatialDomain': {'Geometry': {'GPolygons': [{'Boundary': {'Points': [{'Longitude': -122.33034656,
         'Latitude': 46.86374115},
        {'Longitude': -121.92318169, 'Latitude': 47.84865186},
        {'Longitude': -123.00026736, 'Latitude': 47.85370184},
        {'Longitude': -123.00026241, 'Latitude': 46.86569987},
        {'Longitude': -122.33034656, 'Latitude': 46.86374115}]}}]}}},
 'ProviderDates': [{'Date': '2024-06-25T18:14:02.309Z', 'Type': 'Insert'},
  {'Date': '2024-06-25T18:14:02.309Z', 'Type': 'Update'}],
 'CollectionReference': {'EntryTitle': 'HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0'},
 'RelatedUrls': [{'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B06.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B06.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B06.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B10.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B10.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B10.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B01.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B01.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B01.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.VZA.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.VZA.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.VZA.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.VAA.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.VAA.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.VAA.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B03.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B03.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B03.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B07.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B07.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B07.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B04.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B04.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B04.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.Fmask.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.Fmask.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.Fmask.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B05.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B05.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B05.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B09.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B09.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B09.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.SZA.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.SZA.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.SZA.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B02.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B02.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B02.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.SAA.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.SAA.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.SAA.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B11.tif',
   'Type': 'GET DATA',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.B11.tif'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.B11.tif',
   'Type': 'GET DATA VIA DIRECT ACCESS',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0_stac.json',
   'Type': 'VIEW RELATED INFORMATION',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0_stac.json'},
  {'URL': 's3://lp-prod-public/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0_stac.json',
   'Type': 'VIEW RELATED INFORMATION',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.cmr.xml',
   'Type': 'VIEW RELATED INFORMATION',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.cmr.xml'},
  {'URL': 's3://lp-prod-protected/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.cmr.xml',
   'Type': 'VIEW RELATED INFORMATION',
   'Description': 'This link provides direct download access via S3 to the granule'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/s3credentials',
   'Type': 'VIEW RELATED INFORMATION',
   'Description': 'api endpoint to retrieve temporary credentials valid for same-region direct s3 access'},
  {'URL': 'https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.jpg',
   'Type': 'GET RELATED VISUALIZATION',
   'Description': 'Download HLS.L30.T10TET.2024158T190055.v2.0.jpg'},
  {'URL': 's3://lp-prod-public/HLSL30.020/HLS.L30.T10TET.2024158T190055.v2.0/HLS.L30.T10TET.2024158T190055.v2.0.jpg',
   'Type': 'GET RELATED VISUALIZATION',
   'Description': 'This link provides direct download access via S3 to the granule'}],
 'DataGranule': {'DayNightFlag': 'Day',
  'Identifiers': [{'Identifier': 'HLS.L30.T10TET.2024158T190055',
    'IdentifierType': 'ProducerGranuleId'}],
  'ProductionDateTime': '2024-06-25T18:12:32.000Z',
  'ArchiveAndDistributionInformation': [{'Name': 'Not provided',
    'SizeInBytes': 116048998,
    'SizeUnit': 'MB'}]},
 'Platforms': [{'ShortName': 'LANDSAT-8',
   'Instruments': [{'ShortName': 'OLI'}]}],
 'MetadataSpecification': {'URL': 'https://cdn.earthdata.nasa.gov/umm/granule/v1.6.6',
  'Name': 'UMM-G',
  'Version': '1.6.6'}}
In [ ]:
### Run the function to get granule search results
file_df = get_earthaccess_links(results)
In [15]:
# Check to make sure its a geodataframe
type(file_df)
Out[15]:
geopandas.geodataframe.GeoDataFrame

Step 3c: Open, crop, and mask data¶

In [16]:
### Apply cached decorator to function
@cached('deschutes_reflectance_da_df')

# Write function that computes reflectance data using 
# Search results (df of urls) and watershed boundary
def compute_reflectance_da(search_results, boundary_gdf):

    """Connect to files using VSI, crop them, apply a cloud mask, and wrangle

    Return a single reflectance Dataframe with bands as columns
    and centroid coordinates and dattime as the index
    
    Parameters
    ==========
    search_results:list
        Search result links to the files (urls)
    boundary_gdf: gpd.GeoDataFrame
        Boundary used to crop the data
    """

    # Write a function to open raster from url, apply scale factor, and crop and mask data
    def open_dataarray(url, boundary_proj_gdf, scale = 1, masked = True):

        # Open raster data
        da = rxr.open_rasterio(url, masked = masked).squeeze() * scale  

        # Reproject the boundary needed to match the raster crs
        if boundary_proj_gdf is None:
            boundary_proj_gdf = boundary_gdf.to_crs(da.rio.crs)

        # Crop the raster to the bounding box
        cropped = da.rio.clip_box(*boundary_proj_gdf.total_bounds)

        # Return the cropped image of that tile
        return cropped

    # Write function to apply a cloud mask
    def compute_quality_mask(da, mask_bits = [1, 2, 3]):
        
        """Mask out low quality data by bit"""
        
        # Unpack the bits to a new axis  
        bits = (

            # Unpack each number into individual bits
            np.unpackbits(

                # Convert to 8-bit unsigned integrer format
                da.astype(np.uint8),

                # Set the order of the bits
                bitorder = "little"
            # Reshampe to match original data with an extra dimension for the bits
            ).reshape(da.shape +(-1, ))
        )
        
        # Grab bits we want and check if their flagged
        mask = np.prod(

            # Open bits
            bits[
                ...,
                            mask_bits] == 0,
                            axis = -1)


        # Return the mask
        return mask
    
    # Grab metadata
    file_df = get_earthaccess_links(search_results)

    # Store results for each granule
    granule_da_rows = []

    # Store projected boundary
    boundary_proj_gdf = None

    # Group the data by each granule
    group_iter = file_df.groupby(

        # Datetime and tile_id
        ['datetime', 'tile_id'])
    
    # loop through each image and its metadata
    for (datetime, tile_id), granule_df in tqdm(group_iter):

        # Print status bar
        print(f'Processing granule {tile_id} {datetime}')

        # Find each granule's cloud mask file (fmask) url
        cloud_mask_url = (
            granule_df.loc[granule_df.band == 'Fmask', 'url']
            .values[0])
        
        # Open granule cloud cover
        cloud_masked_cropped_da = open_dataarray(cloud_mask_url, boundary_proj_gdf, scale = 1, masked = False)

        # Compute the cloud mask
        cloud_mask = compute_quality_mask(cloud_masked_cropped_da)
        
        # Loop through each spectral band to open, crop, and mask the band
        da_list = []
        df_list = []
        
        # Lop through each band in the granule
        for i, row in granule_df.iterrows():

            # Only loop through the spectral bands
            if row.band.startswith('B'):

                # Open band's raster and scale to reflectance
                band_cropped = open_dataarray(
                    row.url, boundary_proj_gdf, scale = 0.0001)
                
                # Name the raster by the band
                band_cropped.name = row.band

                # Apply the cloud mask to the raster
                row['da'] = band_cropped.where(cloud_mask)

                # Append the row to the granule_da_rows
                granule_da_rows.append(row.to_frame().T)


    # Reassemble the metadata df
    return pd.concat(granule_da_rows)
In [17]:
### apply the function
reflectance_da_df = compute_reflectance_da(results, deschutes_gdf)
In [18]:
# Check out the dataframe
reflectance_da_df
Out[18]:
datetime tile_id band url geometry da
47 2024-06-06 19:00:55.253000+00:00 T10TDS B10 <File-like object HTTPFileSystem, https://data... POLYGON ((-124.29100013 45.95825869, -122.8740... [[<xarray.DataArray 'B10' ()> Size: 4B\narray(...
48 2024-06-06 19:00:55.253000+00:00 T10TDS B05 <File-like object HTTPFileSystem, https://data... POLYGON ((-124.29100013 45.95825869, -122.8740... [[<xarray.DataArray 'B05' ()> Size: 4B\narray(...
49 2024-06-06 19:00:55.253000+00:00 T10TDS B09 <File-like object HTTPFileSystem, https://data... POLYGON ((-124.29100013 45.95825869, -122.8740... [[<xarray.DataArray 'B09' ()> Size: 4B\narray(...
50 2024-06-06 19:00:55.253000+00:00 T10TDS B01 <File-like object HTTPFileSystem, https://data... POLYGON ((-124.29100013 45.95825869, -122.8740... [[<xarray.DataArray 'B01' ()> Size: 4B\narray(...
51 2024-06-06 19:00:55.253000+00:00 T10TDS B02 <File-like object HTTPFileSystem, https://data... POLYGON ((-124.29100013 45.95825869, -122.8740... [[<xarray.DataArray 'B02' ()> Size: 4B\narray(...
... ... ... ... ... ... ...
1315 2024-08-26 18:55:25.731000+00:00 T10TET B06 <File-like object HTTPFileSystem, https://data... POLYGON ((-121.55985018 46.85663988, -121.5327... [[<xarray.DataArray 'B06' ()> Size: 4B\narray(...
1316 2024-08-26 18:55:25.731000+00:00 T10TET B01 <File-like object HTTPFileSystem, https://data... POLYGON ((-121.55985018 46.85663988, -121.5327... [[<xarray.DataArray 'B01' ()> Size: 4B\narray(...
1317 2024-08-26 18:55:25.731000+00:00 T10TET B11 <File-like object HTTPFileSystem, https://data... POLYGON ((-121.55985018 46.85663988, -121.5327... [[<xarray.DataArray 'B11' ()> Size: 4B\narray(...
1318 2024-08-26 18:55:25.731000+00:00 T10TET B05 <File-like object HTTPFileSystem, https://data... POLYGON ((-121.55985018 46.85663988, -121.5327... [[<xarray.DataArray 'B05' ()> Size: 4B\narray(...
1319 2024-08-26 18:55:25.731000+00:00 T10TET B04 <File-like object HTTPFileSystem, https://data... POLYGON ((-121.55985018 46.85663988, -121.5327... [[<xarray.DataArray 'B04' ()> Size: 4B\narray(...

880 rows × 6 columns

Step 3d: Merge and Composite Data¶

In [19]:
### Apply cache decorator
@cached('deschutes_reflectance_da')

# Create a function to merge and composite reflectance data from multiple granules
# End result: single, composite reflectance image for each spectral band
def merge_and_composite_arrays(granule_da_df):
    
    # Initialize a list to store composites after procesing
    da_list = []

    ### loop over each spectral band
    for band, band_df in granule_da_df.groupby('band'):

        # List for storing merged data arrays (one per date)
        merged_das = []

        # Loop over date/time of image acquisition and merge granules for each date
        for dataetime, date_df in band_df.groupby('datetime'):

            # Merge granules for each date
            merged_da = rxrmerge.merge_arrays(list(date_df.da))
           
            # Mask negative values (could be no data or invalid data)
            merged_da = merged_da.where(merged_da > 0)
            
            # Append to merged_das list we initialized
            merged_das.append(merged_da)
            
        # Composite images across dates
        composite_da = xr.concat(merged_das,
                                 
                                 # Make a datetime dimension
                                 # Calculate median value across the dateimtes for the pixel
                                dim = 'datetime').median('datetime')
        
        # Assign band number to attribute of composite data array
        composite_da['band'] = int(band[1:])

        # Name the composite data array
        composite_da.name = 'reflectance'

        # Add processed and composite data array to lsit
        da_list.append(composite_da)

    # Concatenates composite data arrays for each band along band dimension
    return xr.concat(da_list, dim = 'band')
In [20]:
# Call function to get final composite reflectance data 
reflectance_da = merge_and_composite_arrays(reflectance_da_df)
In [21]:
# Check out the data array
reflectance_da
Out[21]:
<xarray.DataArray 'reflectance' (band: 10, y: 491, x: 561)> Size: 11MB
array([[[0.0151    , 0.0165    , 0.0159    , ..., 0.0497    ,
         0.0528    , 0.0526    ],
        [0.0158    , 0.017     , 0.0168    , ..., 0.056     ,
         0.0624    , 0.0523    ],
        [0.0163    , 0.0175    , 0.0181    , ..., 0.0606    ,
         0.0597    , 0.0473    ],
        ...,
        [0.0356    , 0.0546    , 0.0319    , ..., 0.0083    ,
         0.0087    , 0.0099    ],
        [0.0263    , 0.0487    , 0.0327    , ..., 0.0096    ,
         0.0096    , 0.0097    ],
        [0.0222    , 0.0229    , 0.0201    , ..., 0.0093    ,
         0.0083    , 0.0081    ]],

       [[0.0146    , 0.0159    , 0.0158    , ..., 0.0654    ,
         0.0672    , 0.0657    ],
        [0.0148    , 0.0165    , 0.0167    , ..., 0.0696    ,
         0.0737    , 0.0618    ],
        [0.0152    , 0.0175    , 0.0181    , ..., 0.07169999,
         0.0702    , 0.057     ],
...
        [0.269     , 0.26369998, 0.252     , ..., 0.1988    ,
         0.19849999, 0.19829999],
        [0.25779998, 0.2543    , 0.2446    , ..., 0.1978    ,
         0.1973    , 0.19669999],
        [0.2438    , 0.2417    , 0.2356    , ..., 0.19669999,
         0.1962    , 0.19559999]],

       [[0.1978    , 0.19739999, 0.19749999, ..., 0.2954    ,
         0.2956    , 0.2959    ],
        [0.1964    , 0.1955    , 0.1961    , ..., 0.3074    ,
         0.3065    , 0.305     ],
        [0.1953    , 0.1938    , 0.19469999, ..., 0.30855   ,
         0.3118    , 0.3109    ],
        ...,
        [0.2434    , 0.239     , 0.23249999, ..., 0.1919    ,
         0.1918    , 0.1918    ],
        [0.23279999, 0.23099999, 0.2263    , ..., 0.19119999,
         0.1906    , 0.1901    ],
        [0.22309999, 0.2223    , 0.2191    , ..., 0.19039999,
         0.1894    , 0.18859999]]], shape=(10, 491, 561), dtype=float32)
Coordinates:
  * band         (band) int64 80B 1 2 3 4 5 6 7 9 10 11
  * y            (y) float64 4kB 5.213e+06 5.213e+06 ... 5.198e+06 5.198e+06
  * x            (x) float64 4kB 5.019e+05 5.019e+05 ... 5.187e+05 5.187e+05
    spatial_ref  int64 8B 0
xarray.DataArray
'reflectance'
  • band: 10
  • y: 491
  • x: 561
  • 0.0151 0.0165 0.0159 0.0165 0.0168 ... 0.1911 0.1904 0.1894 0.1886
    array([[[0.0151    , 0.0165    , 0.0159    , ..., 0.0497    ,
         0.0528    , 0.0526    ],
        [0.0158    , 0.017     , 0.0168    , ..., 0.056     ,
         0.0624    , 0.0523    ],
        [0.0163    , 0.0175    , 0.0181    , ..., 0.0606    ,
         0.0597    , 0.0473    ],
        ...,
        [0.0356    , 0.0546    , 0.0319    , ..., 0.0083    ,
         0.0087    , 0.0099    ],
        [0.0263    , 0.0487    , 0.0327    , ..., 0.0096    ,
         0.0096    , 0.0097    ],
        [0.0222    , 0.0229    , 0.0201    , ..., 0.0093    ,
         0.0083    , 0.0081    ]],

       [[0.0146    , 0.0159    , 0.0158    , ..., 0.0654    ,
         0.0672    , 0.0657    ],
        [0.0148    , 0.0165    , 0.0167    , ..., 0.0696    ,
         0.0737    , 0.0618    ],
        [0.0152    , 0.0175    , 0.0181    , ..., 0.07169999,
         0.0702    , 0.057     ],
...
        [0.269     , 0.26369998, 0.252     , ..., 0.1988    ,
         0.19849999, 0.19829999],
        [0.25779998, 0.2543    , 0.2446    , ..., 0.1978    ,
         0.1973    , 0.19669999],
        [0.2438    , 0.2417    , 0.2356    , ..., 0.19669999,
         0.1962    , 0.19559999]],

       [[0.1978    , 0.19739999, 0.19749999, ..., 0.2954    ,
         0.2956    , 0.2959    ],
        [0.1964    , 0.1955    , 0.1961    , ..., 0.3074    ,
         0.3065    , 0.305     ],
        [0.1953    , 0.1938    , 0.19469999, ..., 0.30855   ,
         0.3118    , 0.3109    ],
        ...,
        [0.2434    , 0.239     , 0.23249999, ..., 0.1919    ,
         0.1918    , 0.1918    ],
        [0.23279999, 0.23099999, 0.2263    , ..., 0.19119999,
         0.1906    , 0.1901    ],
        [0.22309999, 0.2223    , 0.2191    , ..., 0.19039999,
         0.1894    , 0.18859999]]], shape=(10, 491, 561), dtype=float32)
    • band
      (band)
      int64
      1 2 3 4 5 6 7 9 10 11
      array([ 1,  2,  3,  4,  5,  6,  7,  9, 10, 11])
    • y
      (y)
      float64
      5.213e+06 5.213e+06 ... 5.198e+06
      array([5212725., 5212695., 5212665., ..., 5198085., 5198055., 5198025.],
      shape=(491,))
    • x
      (x)
      float64
      5.019e+05 5.019e+05 ... 5.187e+05
      array([501915., 501945., 501975., ..., 518655., 518685., 518715.], shape=(561,))
    • spatial_ref
      ()
      int64
      0
      crs_wkt :
      PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]
      semi_major_axis :
      6378137.0
      semi_minor_axis :
      6356752.314245179
      inverse_flattening :
      298.257223563
      reference_ellipsoid_name :
      WGS 84
      longitude_of_prime_meridian :
      0.0
      prime_meridian_name :
      Greenwich
      geographic_crs_name :
      WGS 84
      horizontal_datum_name :
      World Geodetic System 1984
      projected_crs_name :
      WGS 84 / UTM zone 10N
      grid_mapping_name :
      transverse_mercator
      latitude_of_projection_origin :
      0.0
      longitude_of_central_meridian :
      -123.0
      false_easting :
      500000.0
      false_northing :
      0.0
      scale_factor_at_central_meridian :
      0.9996
      spatial_ref :
      PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]
      GeoTransform :
      501900.0 30.0 0.0 5212740.0 0.0 -30.0
      array(0)

STEP 4: K-means clustering¶

In [22]:
### Convert spectral DataArray to a tidy DataFrame
model_df = (reflectance_da
            
            # Flatten the array into a long dataframe
            .to_dataframe()
            
            # Select the reflectance column
            .reflectance
            
            # Make the table wide: each row will be pixel location
            # And each column is a spectral band with the reflectance value
            .unstack('band')
)

# Check dataframe
model_df
Out[22]:
band 1 2 3 4 5 6 7 9 10 11
y x
5212725.0 501915.0 0.0151 0.0146 0.0279 0.0156 0.1930 0.0681 0.0270 0.0009 0.2104 0.1978
501945.0 0.0165 0.0159 0.0314 0.0162 0.2213 0.0787 0.0312 0.0009 0.2123 0.1974
501975.0 0.0159 0.0158 0.0313 0.0163 0.2185 0.0803 0.0304 0.0009 0.2147 0.1975
502005.0 0.0165 0.0158 0.0293 0.0158 0.2173 0.0740 0.0299 0.0010 0.2170 0.2017
502035.0 0.0168 0.0157 0.0346 0.0211 0.2444 0.0989 0.0435 0.0008 0.2184 0.2050
... ... ... ... ... ... ... ... ... ... ... ...
5198025.0 518595.0 0.0093 0.0123 0.0311 0.0175 0.2348 0.0838 0.0350 0.0009 0.1969 0.1916
518625.0 0.0094 0.0122 0.0318 0.0184 0.2515 0.0904 0.0368 0.0009 0.1969 0.1911
518655.0 0.0093 0.0114 0.0301 0.0179 0.2413 0.0878 0.0350 0.0008 0.1967 0.1904
518685.0 0.0083 0.0111 0.0282 0.0162 0.2115 0.0768 0.0320 0.0009 0.1962 0.1894
518715.0 0.0081 0.0104 0.0270 0.0148 0.2052 0.0730 0.0318 0.0010 0.1956 0.1886

275451 rows × 10 columns

In [23]:
# Filter out rows with no data
model_df = model_df.drop(columns = [10,11]).dropna()

# Check the dataframe without NaN
model_df
Out[23]:
band 1 2 3 4 5 6 7 9
y x
5212725.0 501915.0 0.0151 0.0146 0.0279 0.0156 0.1930 0.0681 0.0270 0.0009
501945.0 0.0165 0.0159 0.0314 0.0162 0.2213 0.0787 0.0312 0.0009
501975.0 0.0159 0.0158 0.0313 0.0163 0.2185 0.0803 0.0304 0.0009
502005.0 0.0165 0.0158 0.0293 0.0158 0.2173 0.0740 0.0299 0.0010
502035.0 0.0168 0.0157 0.0346 0.0211 0.2444 0.0989 0.0435 0.0008
... ... ... ... ... ... ... ... ... ...
5198025.0 518595.0 0.0093 0.0123 0.0311 0.0175 0.2348 0.0838 0.0350 0.0009
518625.0 0.0094 0.0122 0.0318 0.0184 0.2515 0.0904 0.0368 0.0009
518655.0 0.0093 0.0114 0.0301 0.0179 0.2413 0.0878 0.0350 0.0008
518685.0 0.0083 0.0111 0.0282 0.0162 0.2115 0.0768 0.0320 0.0009
518715.0 0.0081 0.0104 0.0270 0.0148 0.2052 0.0730 0.0318 0.0010

275357 rows × 8 columns

In [24]:
# Check the data mins and max
min_values = model_df.min()
max_values = model_df.max()

# Print min/max values
print(min_values)
print(max_values)

band
1    0.0002
2    0.0003
3    0.0021
4    0.0001
5    0.0001
6    0.0002
7    0.0002
9    0.0005
dtype: float32
band
1    0.6302
2    0.6953
3    0.7493
4    0.8170
5    0.8450
6    0.9424
7    0.9538
9    0.0016
dtype: float32

Now we're reading to fit the k-means clustering model. We can run the fit and prediction functions at the same time because we don't have target data.

In [25]:
# Initialize k-means model 
k_means = KMeans(n_clusters = 6)

# Fit model and predict
prediction = k_means.fit_predict(model_df.values)

# Add the predicted values back to the model dataframe
model_df['clusters'] = prediction
model_df
Out[25]:
band 1 2 3 4 5 6 7 9 clusters
y x
5212725.0 501915.0 0.0151 0.0146 0.0279 0.0156 0.1930 0.0681 0.0270 0.0009 5
501945.0 0.0165 0.0159 0.0314 0.0162 0.2213 0.0787 0.0312 0.0009 5
501975.0 0.0159 0.0158 0.0313 0.0163 0.2185 0.0803 0.0304 0.0009 5
502005.0 0.0165 0.0158 0.0293 0.0158 0.2173 0.0740 0.0299 0.0010 5
502035.0 0.0168 0.0157 0.0346 0.0211 0.2444 0.0989 0.0435 0.0008 5
... ... ... ... ... ... ... ... ... ... ...
5198025.0 518595.0 0.0093 0.0123 0.0311 0.0175 0.2348 0.0838 0.0350 0.0009 5
518625.0 0.0094 0.0122 0.0318 0.0184 0.2515 0.0904 0.0368 0.0009 5
518655.0 0.0093 0.0114 0.0301 0.0179 0.2413 0.0878 0.0350 0.0008 5
518685.0 0.0083 0.0111 0.0282 0.0162 0.2115 0.0768 0.0320 0.0009 5
518715.0 0.0081 0.0104 0.0270 0.0148 0.2052 0.0730 0.0318 0.0010 5

275357 rows × 9 columns

STEP 5: Plot¶

In [26]:
# Make data array with bands to use for rgb: red, green, and blue
rgb = reflectance_da.sel(band = [4, 3, 2])
In [27]:
# Plot rbg
(
    rgb.hvplot.rgb(y = 'y',
                   x = 'x',
                   bands = 'band',
                   data_aspect = 1,
                   xaxis = None,
                   yaxis = None)
)
Out[27]:
In [28]:
# Stretch values
rgb_uint8 = (rgb * 255).astype(np.uint8).where(rgb != np.nan)

# Look at values
rgb_uint8
Out[28]:
<xarray.DataArray 'reflectance' (band: 3, y: 491, x: 561)> Size: 3MB
array([[[ 3.,  4.,  4., ..., 35., 34., 31.],
        [ 3.,  4.,  4., ..., 33., 32., 27.],
        [ 4.,  4.,  4., ..., 30., 29., 28.],
        ...,
        [16., 25., 12., ...,  4.,  4.,  4.],
        [14., 24., 14., ...,  4.,  4.,  4.],
        [ 9., 11.,  7., ...,  4.,  4.,  3.]],

       [[ 7.,  8.,  7., ..., 25., 26., 23.],
        [ 7.,  8.,  8., ..., 26., 25., 21.],
        [ 7.,  8.,  8., ..., 24., 24., 20.],
        ...,
        [18., 23., 16., ...,  7.,  7.,  8.],
        [16., 22., 15., ...,  8.,  8.,  8.],
        [12., 13., 11., ...,  7.,  7.,  6.]],

       [[ 3.,  4.,  4., ..., 16., 17., 16.],
        [ 3.,  4.,  4., ..., 17., 18., 15.],
        [ 3.,  4.,  4., ..., 18., 17., 14.],
        ...,
        [10., 16.,  9., ...,  2.,  2.,  3.],
        [ 9., 15.,  9., ...,  3.,  3.,  3.],
        [ 6.,  6.,  5., ...,  2.,  2.,  2.]]],
      shape=(3, 491, 561), dtype=float32)
Coordinates:
  * band         (band) int64 24B 4 3 2
  * y            (y) float64 4kB 5.213e+06 5.213e+06 ... 5.198e+06 5.198e+06
  * x            (x) float64 4kB 5.019e+05 5.019e+05 ... 5.187e+05 5.187e+05
    spatial_ref  int64 8B 0
xarray.DataArray
'reflectance'
  • band: 3
  • y: 491
  • x: 561
  • 3.0 4.0 4.0 4.0 5.0 6.0 3.0 4.0 ... 3.0 3.0 3.0 3.0 3.0 2.0 2.0 2.0
    array([[[ 3.,  4.,  4., ..., 35., 34., 31.],
        [ 3.,  4.,  4., ..., 33., 32., 27.],
        [ 4.,  4.,  4., ..., 30., 29., 28.],
        ...,
        [16., 25., 12., ...,  4.,  4.,  4.],
        [14., 24., 14., ...,  4.,  4.,  4.],
        [ 9., 11.,  7., ...,  4.,  4.,  3.]],

       [[ 7.,  8.,  7., ..., 25., 26., 23.],
        [ 7.,  8.,  8., ..., 26., 25., 21.],
        [ 7.,  8.,  8., ..., 24., 24., 20.],
        ...,
        [18., 23., 16., ...,  7.,  7.,  8.],
        [16., 22., 15., ...,  8.,  8.,  8.],
        [12., 13., 11., ...,  7.,  7.,  6.]],

       [[ 3.,  4.,  4., ..., 16., 17., 16.],
        [ 3.,  4.,  4., ..., 17., 18., 15.],
        [ 3.,  4.,  4., ..., 18., 17., 14.],
        ...,
        [10., 16.,  9., ...,  2.,  2.,  3.],
        [ 9., 15.,  9., ...,  3.,  3.,  3.],
        [ 6.,  6.,  5., ...,  2.,  2.,  2.]]],
      shape=(3, 491, 561), dtype=float32)
    • band
      (band)
      int64
      4 3 2
      array([4, 3, 2])
    • y
      (y)
      float64
      5.213e+06 5.213e+06 ... 5.198e+06
      array([5212725., 5212695., 5212665., ..., 5198085., 5198055., 5198025.],
      shape=(491,))
    • x
      (x)
      float64
      5.019e+05 5.019e+05 ... 5.187e+05
      array([501915., 501945., 501975., ..., 518655., 518685., 518715.], shape=(561,))
    • spatial_ref
      ()
      int64
      0
      crs_wkt :
      PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]
      semi_major_axis :
      6378137.0
      semi_minor_axis :
      6356752.314245179
      inverse_flattening :
      298.257223563
      reference_ellipsoid_name :
      WGS 84
      longitude_of_prime_meridian :
      0.0
      prime_meridian_name :
      Greenwich
      geographic_crs_name :
      WGS 84
      horizontal_datum_name :
      World Geodetic System 1984
      projected_crs_name :
      WGS 84 / UTM zone 10N
      grid_mapping_name :
      transverse_mercator
      latitude_of_projection_origin :
      0.0
      longitude_of_central_meridian :
      -123.0
      false_easting :
      500000.0
      false_northing :
      0.0
      scale_factor_at_central_meridian :
      0.9996
      spatial_ref :
      PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]
      GeoTransform :
      501900.0 30.0 0.0 5212740.0 0.0 -30.0
      array(0)
In [29]:
# Plot rbg from 0-255
(
    rgb_uint8.hvplot.rgb(y = 'y',
                   x = 'x',
                   bands = 'band',
                   data_aspect = 1,
                   xaxis = None,
                   yaxis = None)
)
Out[29]:
In [30]:
# Enhance brightness
rgb_uint8_bright = rgb_uint8 * 10

# Look at values
rgb_uint8_bright
Out[30]:
<xarray.DataArray 'reflectance' (band: 3, y: 491, x: 561)> Size: 3MB
array([[[ 30.,  40.,  40., ..., 350., 340., 310.],
        [ 30.,  40.,  40., ..., 330., 320., 270.],
        [ 40.,  40.,  40., ..., 300., 290., 280.],
        ...,
        [160., 250., 120., ...,  40.,  40.,  40.],
        [140., 240., 140., ...,  40.,  40.,  40.],
        [ 90., 110.,  70., ...,  40.,  40.,  30.]],

       [[ 70.,  80.,  70., ..., 250., 260., 230.],
        [ 70.,  80.,  80., ..., 260., 250., 210.],
        [ 70.,  80.,  80., ..., 240., 240., 200.],
        ...,
        [180., 230., 160., ...,  70.,  70.,  80.],
        [160., 220., 150., ...,  80.,  80.,  80.],
        [120., 130., 110., ...,  70.,  70.,  60.]],

       [[ 30.,  40.,  40., ..., 160., 170., 160.],
        [ 30.,  40.,  40., ..., 170., 180., 150.],
        [ 30.,  40.,  40., ..., 180., 170., 140.],
        ...,
        [100., 160.,  90., ...,  20.,  20.,  30.],
        [ 90., 150.,  90., ...,  30.,  30.,  30.],
        [ 60.,  60.,  50., ...,  20.,  20.,  20.]]],
      shape=(3, 491, 561), dtype=float32)
Coordinates:
  * band         (band) int64 24B 4 3 2
  * y            (y) float64 4kB 5.213e+06 5.213e+06 ... 5.198e+06 5.198e+06
  * x            (x) float64 4kB 5.019e+05 5.019e+05 ... 5.187e+05 5.187e+05
    spatial_ref  int64 8B 0
xarray.DataArray
'reflectance'
  • band: 3
  • y: 491
  • x: 561
  • 30.0 40.0 40.0 40.0 50.0 60.0 30.0 ... 30.0 30.0 30.0 20.0 20.0 20.0
    array([[[ 30.,  40.,  40., ..., 350., 340., 310.],
        [ 30.,  40.,  40., ..., 330., 320., 270.],
        [ 40.,  40.,  40., ..., 300., 290., 280.],
        ...,
        [160., 250., 120., ...,  40.,  40.,  40.],
        [140., 240., 140., ...,  40.,  40.,  40.],
        [ 90., 110.,  70., ...,  40.,  40.,  30.]],

       [[ 70.,  80.,  70., ..., 250., 260., 230.],
        [ 70.,  80.,  80., ..., 260., 250., 210.],
        [ 70.,  80.,  80., ..., 240., 240., 200.],
        ...,
        [180., 230., 160., ...,  70.,  70.,  80.],
        [160., 220., 150., ...,  80.,  80.,  80.],
        [120., 130., 110., ...,  70.,  70.,  60.]],

       [[ 30.,  40.,  40., ..., 160., 170., 160.],
        [ 30.,  40.,  40., ..., 170., 180., 150.],
        [ 30.,  40.,  40., ..., 180., 170., 140.],
        ...,
        [100., 160.,  90., ...,  20.,  20.,  30.],
        [ 90., 150.,  90., ...,  30.,  30.,  30.],
        [ 60.,  60.,  50., ...,  20.,  20.,  20.]]],
      shape=(3, 491, 561), dtype=float32)
    • band
      (band)
      int64
      4 3 2
      array([4, 3, 2])
    • y
      (y)
      float64
      5.213e+06 5.213e+06 ... 5.198e+06
      array([5212725., 5212695., 5212665., ..., 5198085., 5198055., 5198025.],
      shape=(491,))
    • x
      (x)
      float64
      5.019e+05 5.019e+05 ... 5.187e+05
      array([501915., 501945., 501975., ..., 518655., 518685., 518715.], shape=(561,))
    • spatial_ref
      ()
      int64
      0
      crs_wkt :
      PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]
      semi_major_axis :
      6378137.0
      semi_minor_axis :
      6356752.314245179
      inverse_flattening :
      298.257223563
      reference_ellipsoid_name :
      WGS 84
      longitude_of_prime_meridian :
      0.0
      prime_meridian_name :
      Greenwich
      geographic_crs_name :
      WGS 84
      horizontal_datum_name :
      World Geodetic System 1984
      projected_crs_name :
      WGS 84 / UTM zone 10N
      grid_mapping_name :
      transverse_mercator
      latitude_of_projection_origin :
      0.0
      longitude_of_central_meridian :
      -123.0
      false_easting :
      500000.0
      false_northing :
      0.0
      scale_factor_at_central_meridian :
      0.9996
      spatial_ref :
      PROJCS["WGS 84 / UTM zone 10N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-123],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32610"]]
      GeoTransform :
      501900.0 30.0 0.0 5212740.0 0.0 -30.0
      array(0)
In [31]:
# Plot brighter rgb
(
    rgb_uint8_bright.hvplot.rgb(y = 'y',
                   x = 'x',
                   bands = 'band',
                   data_aspect = 1,
                   xaxis = None,
                   yaxis = None)
)

WARNING:param.RGBPlot01979: Clipping input data to the valid range for RGB data ([0..1] for floats or [0..255] for integers).
Out[31]:
In [32]:
# Cap saturation
rgb_sat = rgb_uint8_bright.where(rgb_uint8_bright < 255, 255)
In [33]:
# Replot saturated aspect
(
    rgb_sat.hvplot.rgb(y = 'y',
                   x = 'x',
                   bands = 'band',
                   data_aspect = 1,
                   xaxis = None,
                   yaxis = None)
)
Out[33]:
In [34]:
# Plot the clusters from kmeans
(
    model_df.clusters.to_xarray().hvplot(
        x = 'x',
        y = 'y',
        data_aspect = 1,
        xaxis = None,
        yaxis = None)
)
Out[34]:
In [35]:
# Plot the clusters from kmeans, deal with sorting
(
    model_df.clusters.to_xarray().sortby(['x', 'y']).hvplot(
        x = 'x',
        y = 'y',
        data_aspect = 1,
        xaxis = None,
        yaxis = None)
)
Out[35]:
In [36]:
### plot the k-means clusters
(
     rgb_sat.hvplot.rgb(y = 'y',
                   x = 'x',
                   bands = 'band',
                   data_aspect = 1,
                   xaxis = None,
                   yaxis = None,
                   title = 'True Color Reflectance Composite (RGB)')
    + 
    model_df.clusters.to_xarray().sortby(['x', 'y']).hvplot(
        cmap="Colorblind", aspect='equal', title = 'Spectral Reflectance Clusters (K-Means)', data_aspect = 1) 
)
Out[36]:

K-Means Land Cover Classification in Deschutes River - Capitol Lake Watershed in Summer 2024¶

The comparison between the true color RGB image (left) and the k-means spectral reflectance clusters (right) used unsupervised machine learning to explore land-cover classification in the Deschutes River-Capitol Lake watershed. The Deschutes River watershed, and particularly the subwatershed (HUC12 171100160202) that drains into Capitol Lake, is projected to experience significant population growth over the next 20 years. The watershed is already vulnerable to pollution and experiences water quality issues in parameters such as dissolved oxygen, pH, water temperature, fecal coliform, and sediment. Understanding land covers in the area is important for making informed decisions about land use to support watershed management and sustainable growth. The above k-means analysis clustered pixels with similar reflectance values to explore the land cover classifications.

In this geographic area, land covers could potentially include water, wetlands, urban / impervious, agriculture / vegetation, bare soil, and dense forest. While we cannot know the different land covers with certainty, as k-means does not label clusters, the clusters observed in the the k-means analysis do support these classifications. When compared with true color RGB, it does appear to classify different land covers based on their spectral bands accurately. The clusters in the k-means image, cluster 3 is mostly likely open water, cluster 4 could be urban development and impervious surfaces such as roads, cluster 5 aligns with dense forested areas, cluster 2 appears as mixed use vegetation such as agriculture or greenspace, and cluster 1 as additional development. This data analysis was exploratory to look at the surface reflectance data to observe land cover patterns. Land cover classification from satellite imagery is helpful for informing decisions on natural resources management. Future analyses could add additional environmental datasets to refine the clustering, or track temporal changes in clustering over time.

Citations

  • Deschutes River and tributaries - Washington State Department of Ecology. (n.d.). https://ecology.wa.gov/water-shorelines/water-quality/water-improvement/total-maximum-daily-load-process/directory-of-improvement-projects/deschutes-river-and-tributaries#:~:text=The%20Deschutes%20River%20is%20under,process%20to%20address%20these%20problems

  • Land Cover - US EPA. (2025, June 17). US EPA. https://www.epa.gov/report-environment/land-cover#:~:text=The%20EPA%20uses%20land%20cover%20information%20for,Human%20activities%2C%20such%20as:%20*%20Population%20change